Multi-modality is widely used in medical imaging, because it can provide multiinformation
about a target (tumor, organ or tissue). Segmentation using multimodality consists of fusing …

MRI-based medical image analysis for brain tumor studies is gaining attention in recent
times due to an increased need for efficient and objective evaluation of large amounts of …

Multimodality medical imaging techniques have been increasingly applied in clinical
practice and research studies. Corresponding multimodal image analysis and ensemble …

Brain tumor segmentation aims to separate the different tumor tissues such as active cells,
necrotic core, and edema from normal brain tissues of White Matter (WM), Gray Matter (GM) …

Deep learning models such as convolutional neural network have been widely used in 3D
biomedical segmentation and achieve state-of-the-art performance. However, most of them …

Brain tumor pathology is one of the most common mortality issues considered as an
essential priority for health care societies. Accurate diagnosis of the type of disorder is …

The decisive plan in a large number of image processing applications is to take out the
significant features from image data, in which a description, interpretation, or understanding …

Motivated by the recent success in applying deep learning for natural image analysis, we
designed an image segmentation system based on deep Convolutional Neural Network …

We propose a new method that employs transfer learning techniques to effectively correct
sampling selection errors introduced by sparse annotations during supervised learning for …

We consider the problem of brain tumor segmentation from magnetic resonance (MR)
images. This task is most frequently tackled using machine learning methods that generalize …